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Models/Neg-Margin

Neg-Margin

Reported on 8 benchmarks across 2 tasks · 1 paper · 4 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision8 results

  • Image ClassificationonMini-ImageNet to CUB - 5 shot learning
    Accuracy· 2020-03-26
    69.3
    best: 71 (TIM-GD)
    SOTA
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060
  • Image ClassificationonMini-ImageNet - 1-Shot Learning
    Accuracy· 2020-03-26
    63.85
    SOTA
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060
  • Few-Shot Image ClassificationonMini-ImageNet to CUB - 5 shot learning
    Accuracy· 2020-03-26
    69.3
    best: 71 (TIM-GD)
    SOTA
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060
  • Few-Shot Image ClassificationonMini-ImageNet - 1-Shot Learning
    Accuracy· 2020-03-26
    63.85
    SOTA
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060
  • Image ClassificationonCUB 200 5-way 5-shot
    Accuracy· 2020-03-26
    89.4
    best: 98.7 (CAML [Laion-2b])
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060
  • Image ClassificationonCUB 200 5-way 1-shot
    Accuracy· 2020-03-26
    72.66
    best: 95.8 (PT+MAP+SF+SOT (transductive))
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060
  • Few-Shot Image ClassificationonCUB 200 5-way 5-shot
    Accuracy· 2020-03-26
    89.4
    best: 98.7 (CAML [Laion-2b])
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060
  • Few-Shot Image ClassificationonCUB 200 5-way 1-shot
    Accuracy· 2020-03-26
    72.66
    best: 95.8 (PT+MAP+SF+SOT (transductive))
    Negative Margin Matters: Understanding Margin in Few-shot ClassificationarXiv:2003.12060